This map shows the geographic impact of Zheng Wen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zheng Wen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zheng Wen more than expected).
This network shows the impact of papers produced by Zheng Wen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zheng Wen. The network helps show where Zheng Wen may publish in the future.
Co-authorship network of co-authors of Zheng Wen
This figure shows the co-authorship network connecting the top 25 collaborators of Zheng Wen.
A scholar is included among the top collaborators of Zheng Wen based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Zheng Wen. Zheng Wen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Yu, Tong, Branislav Kveton, Zheng Wen, Ruiyi Zhang, & Ole J. Mengshoel. (2020). Graphical Models Meet Bandits: A Variational Thompson Sampling Approach.. International Conference on Machine Learning. 10902–10912.2 indexed citations
2.
Wen, Zheng, et al.. (2020). On Efficiency in Hierarchical Reinforcement Learning. Neural Information Processing Systems. 33. 6708–6718.9 indexed citations
Gupta, Prakhar, et al.. (2019). Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank.. Uncertainty in Artificial Intelligence. 722–732.3 indexed citations
5.
Osband, Ian, Benjamin Van Roy, Daniel Russo, & Zheng Wen. (2019). Deep Exploration via Randomized Value Functions. Journal of Machine Learning Research. 20(124). 1–62.47 indexed citations
Kveton, Branislav, et al.. (2014). Matroid bandits: fast combinatorial optimization with learning. Uncertainty in Artificial Intelligence. 420–429.27 indexed citations
17.
Kveton, Branislav, et al.. (2014). Matroid Bandits: Practical Large-Scale Combinatorial Bandits. National Conference on Artificial Intelligence.2 indexed citations
18.
Gabillon, Victor, Branislav Kveton, Zheng Wen, Brian Eriksson, & S. Muthukrishnan. (2013). Adaptive Submodular Maximization in Bandit Setting. neural information processing systems. 26. 2697–2705.20 indexed citations
19.
Wen, Zheng & Benjamin Van Roy. (2013). Efficient Exploration and Value Function Generalization in Deterministic Systems. Neural Information Processing Systems. 26. 3021–3029.5 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.